Local search genetic algorithm for optimal design of reliable networks

Dengız, Berna; Altıparmak, Fulya; Smith, Alice E.

doi:10.1109/4235.661548

Cited by 215 publications

(131 citation statements)

References 28 publications

Supporting

Mentioning

120

Contrasting

Unclassified

Order By: Relevance

“…For R min =0.87, Fig. 1 (b) shows the optimal network topology, G min , whose links form a set of spanning trees{ (2,5,4), (1,4,5), (2, 1, 4), (1, 2, 5)} with Rel(G min )=0.88 and Cost(G min )=18; G min does not contain spanning trees (1,3,4), (1,3,5), (2,3,5) and (2, 3, 4) because link 3 is not selected.…”

Section: Network Design Problem and Solutionmentioning

confidence: 99%

“…As shown in the table, such approach produces only 18.4% topologies with less optimal cost. Table IV compares the effectiveness of our DPCR-ST against four state-of-the-art approaches, i.e., NGA [2], LS/NG [3], ACO-SA [5], and BDD [11], using the 76 fully connected networks. Since we were unable to obtain the source codes for the four approaches in [2], [3], [5], and [11], we have used their reported results in our comparisons.…”

Section: A the Effect Of Spanning Tree Orderings On The Performance mentioning

confidence: 99%

“…There are many proposed solutions for NTD-CR problem. The existing algorithms that generate approximation solution are mainly based on meta-heuristic techniques, e.g., Genetic Algorithm [2], [3], Swarm Particle [4] and Ant Colony [5]. While the metaheuristic algorithms can significantly reduce time complexity, they still require numerous iterations to converge and thus use a considerable computational effort while producing only up to 63.1% optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

“…The NTD-CR problem has been shown NP-hard [3], and thus one must use heuristic and/or approximation solutions to design large sized topologies. There are many proposed solutions for NTD-CR problem.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Programming for Minimal Cost Topology with Reliability Constraint

Elshqeirat¹,

Soh²,

Rai³

et al. 2014

JACN

View full text Add to dashboard Cite

Abstract-This paper addresses an NP-hard problem, called NTD-CR, to design a minimal-cost communication network topology that satisfies a pre-defined reliability constraint. Since reliability is always a major issue in the network design, the problem is practical for critical applications requiring minimized cost. The paper formulates a dynamic programming (DP) scheme to solve NTD-CR problem. DP approach, called DPCR-ST, generates the topology using a selected set of spanning trees of the network, STX min . We propose three greedy heuristics to generate and order only k spanning trees of the network. Each heuristic allows DPCR-ST to enumerate STX min using only k spanning trees, which improves the time complexity while producing near optimal topology. Simulations based on fully connected networks that contain up to 2.3×10 9 spanning trees show the merits of ordering methods and the effectiveness of our algorithm vis-à-vis four existing state-of-the-art techniques; DPCR-ST produces 81.5% optimal results, while using only 0.77%of the spanning trees contained in network.Index Terms-Dynamic programing, network optimization, network reliability, network topology design.

show abstract

Section: Network Design Problem and Solutionmentioning

confidence: 99%

Section: A the Effect Of Spanning Tree Orderings On The Performance mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Programming for Minimal Cost Topology with Reliability Constraint

Elshqeirat¹,

Soh²,

Rai³

et al. 2014

JACN

View full text Add to dashboard Cite

show abstract

“…These 1000 evaluations required 20.5 minutes, an average of 1.23 seconds per evaluation. According to Equation 1, the search space size is 1.3x10 7 .…”

Section: Illustrative Examplesmentioning

confidence: 99%

Adaptive reconfiguration of data networks using genetic algorithms

Montaña¹,

Hussain²

2004

Applied Soft Computing

View full text Add to dashboard Cite

Genetic algorithms are applied to an important, but little-investigated, network design problem, that of recon guring the topology and link capacities of an operational network to adapt to changes in its operating conditions. These conditions include: which nodes and links are unavailable the tra c patterns and the quality of service (QoS) requirements and priorities of di erent users and applications. Dynamic recon guration is possible in networks that contain links whose endpoints can be easily changed, such as satellite channels or terrestrial wireless connections. We report results that demonstrate the feasibility of performing genetic search quickly enough for online adaptation.

show abstract

Intelligent Flow Control under a Game Theoretic Framework

Chen

2001

Telecommunications Optimization: Heuristic and Adaptive Techniques

View full text Add to dashboard Cite

Each of us is interested in optimization, and telecommunications. Via several meetings, conferences, chats, and other opportunities, we have discovered these joint interests and decided to put together this book.It certainly wasn't easy. What made it difficult is partly the reason for this book in the first place: things are moving fast! As researchers in both industry and academia, we are under constant pressure to keep up with new technologies and opportunities. Part of our day jobs involves developing good algorithms for a steady surge of new problems arising from industry. Another part of our day jobs involves finding realistic problems which might yield to some of the algorithms we have developed, which we particularly like, but which don't work on anything else. The remainder of our day jobs tends to involve lecturing (for two of us), project management (for all of us), and things like exam-setting and marking. Finally, for two of us, childcare takes up a substantial part of our night jobs. Naturally, the book was finished when we worked out how to stuff 46 hours into a single day, and keep that up for a few successive weeks.Seriously, though, telecommunicatons is growing and changing very quickly, and all of this involves the increasing availability of new real-world problems for optimization researchers to address. Telecommunications engineers tend to know all about the problems involved, but are often not aware of developments in computer science and artificial intelligence which might go a long way towards solving those problems. We therefore argue, at the beginning of Chapter 1, that there needs to be more collaboration between the communities, so that both sides can be equipped for the challenges ahead. Indeed, this book is the result of one such collaboration.We have oriented it slightly towards the telecommunications engineer who knows little about the computational techniques, rather than the other way around. This is mainly because it is much easier to describe the techniques than it is to describe the telecommunications issues; the latter is a much larger collection. However, individual chapters go into some detail, often at an introductory level, in describing particular kinds of problem. Also, we provide a brief introduction to the essence of the problems at the beginning of each part.Let us know what you think. In particular, if you are a telecommunications engineer, and have been inspired by a chapter or two in this book to try out some techniques that you otherwise wouldn't have, please get in touch: your story may make it into the next edition!

show abstract

Local search genetic algorithm for optimal design of reliable networks

Cited by 215 publications

References 28 publications

Dynamic Programming for Minimal Cost Topology with Reliability Constraint

Dynamic Programming for Minimal Cost Topology with Reliability Constraint

Adaptive reconfiguration of data networks using genetic algorithms

Intelligent Flow Control under a Game Theoretic Framework

Contact Info

Product

Resources

About